Automated Scalable Contextual Data Collection and Extraction System

1. A system for data loss prevention, comprising: a computing device comprising a memory, a processor, and a non-volatile data storage device; an extraction engine comprising a first plurality of programming instructions stored in the memory and operable on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to: receive a context for monitoring of network traffic; monitor the network traffic scheduled to leave a computer network associated with the context; send extracted data from the monitored traffic related to the context to a knowledge graph construction service and to a data loss preventer; an knowledge graph construction service comprising a second plurality of programming instructions stored in the memory and operable on the processor, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to: receive the extracted data from the extraction engine; create and maintain a knowledge graph from the extracted data, the knowledge graph comprises vertices representing data points from the extracted data and edges representing relationships either between the data points or between data points and the context; and store the knowledge graph in the non-volatile data storage device; and a data loss preventer comprising a third plurality of programming instructions stored in the memory and operable on the processor, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to: receive the data from the extraction engine; identify a relationship between the extracted data and data contained within the knowledge graph related to the context; and prevent the extracted data from leaving the computer network.

2. The system of claim 1 , wherein the context comprises a type of data to be monitored.

3. The system of claim 2 , wherein the type of data to be monitored comprises personally identifiable information.

4. The system of claim 3 , wherein extracted data comprises personally identifiable information associated with a universal unique identifier.

5. The system of claim 4 , wherein the relationship between the extracted data and data contained within the knowledge graph is a universal unique identifier that is common to two or more pieces of personally identifiable information.

6. A method for data loss prevention, comprising the steps of: receiving a context for monitoring of network traffic from an extraction engine operating on a computing device comprising a memory, a processor, and a non-volatile data storage device; monitoring the network traffic scheduled to leave a computer network associated with the context using the extraction engine; sending extracted data from the monitored traffic related to the context to a knowledge graph construction service and to a data loss preventer, both operating on the computing device; creating and maintaining a knowledge graph from the extracted data using the knowledge graph construction service, the knowledge graph comprising vertices representing data points from the extracted data and edges representing relationships either between the data points or between data points and the context; storing the knowledge graph in the non-volatile data storage device; and identifying a relationship between the extracted data and data contained within the knowledge graph related to the context using the data loss preventer; and preventing the extracted data from leaving the computer network.

7. The method of claim 6 , wherein the context comprises a type of data to be monitored.

8. The method of claim 7 , wherein the type of data to be monitored comprises personally identifiable information.

9. The method of claim 8 , wherein extracted data comprises personally identifiable information associated with a universal unique identifier.

10. The method of claim 9 , wherein the relationship between the extracted data and data contained within the knowledge graph is a universal unique identifier that is common to two or more pieces of personally identifiable information.